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Title: Enhancing Urban Flow Maps via Neural ODEs

Flow super-resolution (FSR) enables inferring fine-grained urban flows with coarse-grained observations and plays an important role in traffic monitoring and prediction. The existing FSR solutions rely on deep CNN models (e.g., ResNet) for learning spatial correlation, incurring excessive memory cost and numerous parameter updates. We propose to tackle the urban flows inference using dynamic systems paradigm and present a new method FODE -- FSR with Ordinary Differential Equations (ODEs). FODE extends neural ODEs by introducing an affine coupling layer to overcome the problem of numerically unstable gradient computation, which allows more accurate and efficient spatial correlation estimation, without extra memory cost. In addition, FODE provides a flexible balance between flow inference accuracy and computational efficiency. A FODE-based augmented normalization mechanism is further introduced to constrain the flow distribution with the influence of external factors. Experimental evaluations on two real-world datasets demonstrate that FODE significantly outperforms several baseline approaches.

Authors:
; ; ; ; ;
Award ID(s):
1823279
Publication Date:
NSF-PAR ID:
10211118
Journal Name:
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI} 2020
Page Range or eLocation-ID:
1295 to 1302
Sponsoring Org:
National Science Foundation
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